Mobile Robot Localization Using Optical Flow Sensors

Open-loop position estimation methods are commonly used in mobile robot applications. Their strength lies in the speed and simplicity with which an estimated position is determined. However, these methods can lead to inaccurate or unreliable estimates. Two position estimation methods are developed in this paper, one using a single optical flow sensor and a second using two optical sensors. The first method can accurately estimate position under ideal conditions and also when wheel slip perpendicular to the axis of the wheel occurs. The second method can accurately estimate position even when wheel slip parallel to the axis of the wheel occurs. Location of the sensors is investigated in order to minimize errors caused by inaccurate sensor readings. Finally, a method is implemented and tested using a potential field based navigation scheme. Estimates of position were found to be as accurate as dead-reckoning in ideal conditions and much more accurate in cases where wheel slip occurs.

[1]  Hee-Jun Kang,et al.  Necessary and Sufficient Stability Condition of Discrete State Delay Systems , 2004 .

[2]  Hugh F. Durrant-Whyte,et al.  Inertial navigation systems for mobile robots , 1995, IEEE Trans. Robotics Autom..

[3]  Gabriel Ramírez,et al.  A new local path planner for nonholonomic mobile robot navigation in cluttered environments , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[4]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[5]  Bradford W. Parkinson,et al.  Global positioning system : theory and applications , 1996 .

[6]  Piotr Ptasinski,et al.  A method for dead reckoning parameter correction in pedestrian navigation system , 2003, IEEE Trans. Instrum. Meas..

[7]  Jinsung Oh Structuring Element Representation of an Image and Its Applications , 2004 .

[8]  Liqiang Feng,et al.  Measurement and correction of systematic odometry errors in mobile robots , 1996, IEEE Trans. Robotics Autom..

[9]  Keiji Nagatani,et al.  Improvement of odometry for omnidirectional vehicle using optical flow information , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[10]  Penny Probert Smith,et al.  A stereo vision-based aid for the visually impaired , 1998, Image Vis. Comput..

[11]  W. Brogan Modern Control Theory , 1971 .

[12]  Bradford W. Parkinson,et al.  Global Positioning System , 1995 .

[13]  Linda G. Shapiro,et al.  Computer Vision , 2001 .

[14]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[15]  R. Jirawimut,et al.  A method for dead reckoning parameter correction in pedestrian navigation system , 2001, IMTC 2001. Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics (Cat. No.01CH 37188).